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stepgbm  

Stepwise Variable Selection for Generalized Boosted Regression Modeling
View on CRAN: Click here


Download and install stepgbm package within the R console
Install from CRAN:
install.packages("stepgbm")

Install from Github:
library("remotes")
install_github("cran/stepgbm")

Install by package version:
library("remotes")
install_version("stepgbm", "1.0.1")



Attach the package and use:
library("stepgbm")
Maintained by
Jin Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-12-10
Latest Update: 2023-04-04
Description:
An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <doi:10.13140/RG.2.2.27686.22085>.
How to cite:
Jin Li (2021). stepgbm: Stepwise Variable Selection for Generalized Boosted Regression Modeling. R package version 1.0.1, https://cran.r-project.org/web/packages/stepgbm. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0.0 (2021-12-10 09:20)
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Complete documentation for stepgbm
Functions, R codes and Examples using the stepgbm R package
Some associated functions: cran-comments . stepgbm . stepgbmRVI . 
Some associated R codes: stepgbm.R . stepgbmRVI.R .  Full stepgbm package functions and examples
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